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<div class="title">TensorExecutor.h</div>  </div>
</div><!--header-->
<div class="contents">
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">// This file is part of Eigen, a lightweight C++ template library</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// for linear algebra.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">// Copyright (C) 2014 Benoit Steiner &lt;benoit.steiner.goog@gmail.com&gt;</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment">// This Source Code Form is subject to the terms of the Mozilla</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment">// Public License v. 2.0. If a copy of the MPL was not distributed</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment">// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160; </div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160; </div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &quot;./InternalHeaderCheck.h&quot;</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160; </div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespaceEigen.html">Eigen</a> {</div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160; </div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="comment">// TODO(ezhulenev): Add specializations for all other types of Tensor ops.</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160; </div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Expression&gt;</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="keyword">struct </span>ExpressionHasTensorBroadcastingOp {</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  <span class="keyword">enum</span> { value = <span class="keyword">false</span> };</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;};</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160; </div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> LhsXprType, <span class="keyword">typename</span> RhsXprType&gt;</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="keyword">struct </span>ExpressionHasTensorBroadcastingOp&lt;</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    const TensorAssignOp&lt;LhsXprType, RhsXprType&gt; &gt; {</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  <span class="keyword">enum</span> { value = ExpressionHasTensorBroadcastingOp&lt;RhsXprType&gt;::value };</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;};</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160; </div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> UnaryOp, <span class="keyword">typename</span> XprType&gt;</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="keyword">struct </span>ExpressionHasTensorBroadcastingOp&lt;</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    const TensorCwiseUnaryOp&lt;UnaryOp, XprType&gt; &gt; {</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  <span class="keyword">enum</span> { value = ExpressionHasTensorBroadcastingOp&lt;XprType&gt;::value };</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;};</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> BinaryOp, <span class="keyword">typename</span> LhsXprType, <span class="keyword">typename</span> RhsXprType&gt;</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="keyword">struct </span>ExpressionHasTensorBroadcastingOp&lt;</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    const TensorCwiseBinaryOp&lt;BinaryOp, LhsXprType, RhsXprType&gt; &gt; {</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    value = ExpressionHasTensorBroadcastingOp&lt;LhsXprType&gt;::value ||</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;        ExpressionHasTensorBroadcastingOp&lt;RhsXprType&gt;::value</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;  };</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;};</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160; </div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Broadcast, <span class="keyword">typename</span> XprType&gt;</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="keyword">struct </span>ExpressionHasTensorBroadcastingOp&lt;</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    const TensorBroadcastingOp&lt;Broadcast, XprType&gt; &gt; {</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  <span class="keyword">enum</span> { value = <span class="keyword">true</span> };</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;};</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160; </div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="comment">// -------------------------------------------------------------------------- //</span></div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160; </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keyword">typename</span> Device, <span class="keywordtype">bool</span> Vectorizable,</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;          TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a> {</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Expression::Index StorageIndex;</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160; </div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  <span class="comment">// Including `unsupported/Eigen/CXX11/Tensor` in different translation units</span></div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <span class="comment">// with/without `EIGEN_USE_THREADS` or `EIGEN_USE_GPU` is a potential ODR</span></div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  <span class="comment">// violation. If this template is instantiated with a non-default device, it</span></div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  <span class="comment">// means that this header file was included without defining</span></div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  <span class="comment">// `EIGEN_USE_THREADS`, `EIGEN_USE_GPU` or `EIGEN_USE_SYCL`.</span></div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  static_assert(std::is_same&lt;Device, DefaultDevice&gt;::value,</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;                <span class="stringliteral">&quot;Default executor instantiated with non-default device. &quot;</span></div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;                <span class="stringliteral">&quot;You must #define EIGEN_USE_THREADS, EIGEN_USE_GPU or &quot;</span></div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;                <span class="stringliteral">&quot;EIGEN_USE_SYCL before including Eigen headers.&quot;</span>);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160; </div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> run(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;                                      <span class="keyword">const</span> Device&amp; device = Device()) {</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    TensorEvaluator&lt;Expression, Device&gt; evaluator(expr, device);</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      <span class="keyword">const</span> StorageIndex size = array_prod(evaluator.dimensions());</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      <span class="keywordflow">for</span> (StorageIndex i = 0; i &lt; size; ++i) {</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        evaluator.evalScalar(i);</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;      }</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    }</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    evaluator.cleanup();</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  }</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;};</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keyword">typename</span> Device, <span class="keyword">typename</span> DoneCallback,</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;          <span class="keywordtype">bool</span> Vectorizable, TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;<span class="keyword">class </span>TensorAsyncExecutor {};</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression&gt;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a>&lt;Expression, DefaultDevice, <span class="comment">/*Vectorizable=*/</span>true,</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;                     <span class="comment">/*Tiling=*/</span>TiledEvaluation::Off&gt; {</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Expression::Index StorageIndex;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> run(</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;      <span class="keyword">const</span> Expression&amp; expr, <span class="keyword">const</span> DefaultDevice&amp; device = DefaultDevice()) {</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    TensorEvaluator&lt;Expression, DefaultDevice&gt; evaluator(expr, device);</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;      <span class="keyword">const</span> StorageIndex size = array_prod(evaluator.dimensions());</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> PacketSize = unpacket_traits&lt;<span class="keyword">typename</span> TensorEvaluator&lt;</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;          Expression, DefaultDevice&gt;::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;      <span class="comment">// Give compiler a strong possibility to unroll the loop. But don&#39;t insist</span></div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;      <span class="comment">// on unrolling, because if the function is expensive compiler should not</span></div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;      <span class="comment">// unroll the loop at the expense of inlining.</span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;      <span class="keyword">const</span> StorageIndex UnrolledSize =</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;          (size / (4 * PacketSize)) * 4 * PacketSize;</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;      <span class="keywordflow">for</span> (StorageIndex i = 0; i &lt; UnrolledSize; i += 4 * PacketSize) {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;        <span class="keywordflow">for</span> (StorageIndex j = 0; j &lt; 4; j++) {</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;          evaluator.evalPacket(i + j * PacketSize);</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;        }</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      }</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      <span class="keyword">const</span> StorageIndex VectorizedSize = (size / PacketSize) * PacketSize;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;      <span class="keywordflow">for</span> (StorageIndex i = UnrolledSize; i &lt; VectorizedSize; i += PacketSize) {</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        evaluator.evalPacket(i);</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      }</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      <span class="keywordflow">for</span> (StorageIndex i = VectorizedSize; i &lt; size; ++i) {</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        evaluator.evalScalar(i);</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;      }</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    }</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    evaluator.cleanup();</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;  }</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;};</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keywordtype">bool</span> Vectorizable&gt;</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a>&lt;Expression, DefaultDevice, Vectorizable,</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;                     <span class="comment">/*Tiling=*/</span>TiledEvaluation::On&gt; {</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits&lt;Expression&gt;::Scalar Scalar;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  <span class="keyword">typedef</span> std::remove_const_t&lt;Scalar&gt; ScalarNoConst;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160; </div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;Expression, DefaultDevice&gt; Evaluator;</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits&lt;Expression&gt;::Index StorageIndex;</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDims = traits&lt;Expression&gt;::NumDimensions;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160; </div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> run(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;                         <span class="keyword">const</span> DefaultDevice&amp; device = DefaultDevice()) {</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <span class="keyword">typedef</span> TensorBlockMapper&lt;NumDims, Evaluator::Layout, StorageIndex&gt;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        TensorBlockMapper;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160; </div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keyword">typedef</span> internal::TensorBlockDescriptor&lt;NumDims, StorageIndex&gt;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;        TensorBlockDesc;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="keyword">typedef</span> internal::TensorBlockScratchAllocator&lt;DefaultDevice&gt;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        TensorBlockScratch;</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160; </div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    Evaluator evaluator(expr, device);</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160; </div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="comment">// TODO(ezhulenev): Do not use tiling for small tensors?</span></div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;      <span class="comment">// Query expression tree for desired block size/shape.</span></div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;      <span class="keyword">const</span> TensorBlockResourceRequirements requirements =</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;          evaluator.getResourceRequirements();</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160; </div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;      <span class="keyword">const</span> TensorBlockMapper block_mapper(</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;          <span class="keyword">typename</span> TensorBlockDesc::Dimensions(evaluator.dimensions()),</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;          requirements);</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160; </div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;      <span class="comment">// Share scratch memory allocator between all blocks.</span></div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      TensorBlockScratch scratch(device);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160; </div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;      <span class="keyword">const</span> StorageIndex total_block_count = block_mapper.blockCount();</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;      <span class="keywordflow">for</span> (StorageIndex i = 0; i &lt; total_block_count; ++i) {</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        TensorBlockDesc desc = block_mapper.blockDescriptor(i);</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        evaluator.evalBlock(desc, scratch);</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        scratch.reset();</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;      }</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    }</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    evaluator.cleanup();</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;  }</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;};</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160; </div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;<span class="preprocessor">#ifdef EIGEN_USE_THREADS</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160; </div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> TensorBlockMapper&gt;</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;<span class="keyword">struct </span>TensorExecutorTilingContext {</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;  TensorExecutorTilingContext() = <span class="keywordflow">default</span>;</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;  TensorExecutorTilingContext(<span class="keyword">const</span> TensorBlockMapper&amp; b_mapper,</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;                              <span class="keyword">const</span> TensorOpCost&amp; b_cost, <span class="keywordtype">size_t</span> b_aligned_size)</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      : block_mapper(b_mapper),</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        cost(b_cost),</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;        aligned_blocksize(b_aligned_size) {}</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  TensorBlockMapper block_mapper;  <span class="comment">// navigate through blocks</span></div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;  TensorOpCost cost;               <span class="comment">// cost of computing a single block</span></div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;  <span class="keywordtype">size_t</span> aligned_blocksize;        <span class="comment">// block size after memory alignment</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;};</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160; </div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;<span class="comment">// Computes a block evaluation parameters, and allocates temporary memory buffer</span></div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;<span class="comment">// for blocks. See TensorExecutor/TensorAsyncExecutor (Tiling=On) below.</span></div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> TensorBlockMapper, <span class="keywordtype">bool</span> Vectorizable&gt;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;TensorExecutorTilingContext&lt;TensorBlockMapper&gt; GetTensorExecutorTilingContext(</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    <span class="keyword">const</span> Evaluator&amp; evaluator) {</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  <span class="comment">// Query expression tree for desired block size/shape.</span></div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;  TensorBlockResourceRequirements requirements =</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      evaluator.getResourceRequirements();</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;  <span class="comment">// Update target block size based on cost model.</span></div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;  <span class="keywordtype">double</span> taskSize = TensorCostModel&lt;ThreadPoolDevice&gt;::taskSize(</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;      1, requirements.cost_per_coeff);</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;  requirements.size = <span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(1.0 / taskSize);</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160; </div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;  TensorBlockMapper block_mapper(</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;      <span class="keyword">typename</span> TensorBlockMapper::Dimensions(evaluator.dimensions()),</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;      requirements);</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160; </div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;  <span class="keywordtype">size_t</span> block_size = block_mapper.blockTotalSize();</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">size_t</span> aligned_blocksize =</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;      align *</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      divup&lt;size_t&gt;(block_size * <span class="keyword">sizeof</span>(<span class="keyword">typename</span> Evaluator::Scalar), align);</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160; </div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;  <span class="keywordflow">return</span> {block_mapper, requirements.cost_per_coeff * block_size,</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;          aligned_blocksize};</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;}</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160; </div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> StorageIndex, <span class="keywordtype">bool</span> Vectorizable&gt;</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;<span class="keyword">struct </span>EvalRange {</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">void</span> run(Evaluator* evaluator_in, <span class="keyword">const</span> StorageIndex firstIdx,</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;                  <span class="keyword">const</span> StorageIndex lastIdx) {</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    Evaluator evaluator = *evaluator_in;</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    eigen_assert(lastIdx &gt;= firstIdx);</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="keywordflow">for</span> (StorageIndex i = firstIdx; i &lt; lastIdx; ++i) {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;      evaluator.evalScalar(i);</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    }</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;  }</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160; </div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  <span class="keyword">static</span> StorageIndex alignBlockSize(StorageIndex size) { <span class="keywordflow">return</span> size; }</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;};</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160; </div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> StorageIndex&gt;</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;<span class="keyword">struct </span>EvalRange&lt;Evaluator, StorageIndex, <span class="comment">/*Vectorizable*/</span> true&gt; {</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> PacketSize =</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      unpacket_traits&lt;typename Evaluator::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160; </div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">void</span> run(Evaluator* evaluator_in, <span class="keyword">const</span> StorageIndex firstIdx,</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;                  <span class="keyword">const</span> StorageIndex lastIdx) {</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    Evaluator evaluator = *evaluator_in;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    eigen_assert(lastIdx &gt;= firstIdx);</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    StorageIndex i = firstIdx;</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    <span class="keywordflow">if</span> (lastIdx - firstIdx &gt;= PacketSize) {</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;      eigen_assert(firstIdx % PacketSize == 0);</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;      StorageIndex last_chunk_offset = lastIdx - 4 * PacketSize;</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;      <span class="comment">// Give compiler a strong possibility to unroll the loop. But don&#39;t insist</span></div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      <span class="comment">// on unrolling, because if the function is expensive compiler should not</span></div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;      <span class="comment">// unroll the loop at the expense of inlining.</span></div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= last_chunk_offset; i += 4 * PacketSize) {</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        <span class="keywordflow">for</span> (StorageIndex j = 0; j &lt; 4; j++) {</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;          evaluator.evalPacket(i + j * PacketSize);</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        }</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;      }</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;      last_chunk_offset = lastIdx - PacketSize;</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;      <span class="keywordflow">for</span> (; i &lt;= last_chunk_offset; i += PacketSize) {</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        evaluator.evalPacket(i);</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;      }</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    }</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="keywordflow">for</span> (; i &lt; lastIdx; ++i) {</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;      evaluator.evalScalar(i);</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    }</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;  }</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160; </div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;  <span class="keyword">static</span> StorageIndex alignBlockSize(StorageIndex size) {</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    <span class="comment">// Align block size to packet size and account for unrolling in run above.</span></div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    <span class="keywordflow">if</span> (size &gt;= 16 * PacketSize) {</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;      <span class="keywordflow">return</span> (size + 4 * PacketSize - 1) &amp; ~(4 * PacketSize - 1);</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    }</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="comment">// Aligning to 4 * PacketSize would increase block size by more than 25%.</span></div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <span class="keywordflow">return</span> (size + PacketSize - 1) &amp; ~(PacketSize - 1);</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  }</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;};</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160; </div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keywordtype">bool</span> Vectorizable, TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a>&lt;Expression, ThreadPoolDevice, Vectorizable, Tiling&gt; {</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Expression::Index StorageIndex;</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160; </div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> run(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;                         <span class="keyword">const</span> ThreadPoolDevice&amp; device) {</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <span class="keyword">typedef</span> TensorEvaluator&lt;Expression, ThreadPoolDevice&gt; Evaluator;</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keyword">typedef</span> EvalRange&lt;Evaluator, StorageIndex, Vectorizable&gt; EvalRange;</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160; </div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    Evaluator evaluator(expr, device);</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(<span class="keyword">nullptr</span>);</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;      <span class="keyword">const</span> StorageIndex size = array_prod(evaluator.dimensions());</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;      device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;                         EvalRange::alignBlockSize,</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;                         [&amp;evaluator](StorageIndex firstIdx, StorageIndex lastIdx) {</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;                           EvalRange::run(&amp;evaluator, firstIdx, lastIdx);</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;                         });</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;    }</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    evaluator.cleanup();</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;  }</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;};</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160; </div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keywordtype">bool</span> Vectorizable&gt;</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a>&lt;Expression, ThreadPoolDevice, Vectorizable,</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;                     <span class="comment">/*Tiling=*/</span>TiledEvaluation::On&gt; {</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits&lt;Expression&gt;::Index IndexType;</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits&lt;Expression&gt;::Scalar Scalar;</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  <span class="keyword">typedef</span> std::remove_const_t&lt;Scalar&gt; ScalarNoConst;</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160; </div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDims = traits&lt;Expression&gt;::NumDimensions;</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160; </div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;Expression, ThreadPoolDevice&gt; Evaluator;</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;  <span class="keyword">typedef</span> TensorBlockMapper&lt;NumDims, Evaluator::Layout, IndexType&gt; BlockMapper;</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;  <span class="keyword">typedef</span> TensorExecutorTilingContext&lt;BlockMapper&gt; TilingContext;</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160; </div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockDescriptor&lt;NumDims, IndexType&gt;</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;      TensorBlockDesc;</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockScratchAllocator&lt;ThreadPoolDevice&gt;</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;      TensorBlockScratch;</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160; </div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> run(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;                                      <span class="keyword">const</span> ThreadPoolDevice&amp; device) {</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    Evaluator evaluator(expr, device);</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160; </div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(<span class="keyword">nullptr</span>);</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;      <span class="keyword">const</span> TilingContext tiling =</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;          internal::GetTensorExecutorTilingContext&lt;Evaluator, BlockMapper,</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;                                                   Vectorizable&gt;(evaluator);</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160; </div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;      <span class="keyword">auto</span> eval_block = [&amp;device, &amp;evaluator, &amp;tiling](IndexType firstBlockIdx,</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;                                                       IndexType lastBlockIdx) {</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        TensorBlockScratch scratch(device);</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160; </div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        <span class="keywordflow">for</span> (IndexType block_idx = firstBlockIdx; block_idx &lt; lastBlockIdx;</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;             ++block_idx) {</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;          TensorBlockDesc desc = tiling.block_mapper.blockDescriptor(block_idx);</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;          evaluator.evalBlock(desc, scratch);</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;          scratch.reset();</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;        }</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;      };</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160; </div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;      <span class="comment">// Evaluate small expressions directly as a single block.</span></div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;      <span class="keywordflow">if</span> (tiling.block_mapper.blockCount() == 1) {</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;        TensorBlockScratch scratch(device);</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;        TensorBlockDesc desc(0, tiling.block_mapper.blockDimensions());</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;        evaluator.evalBlock(desc, scratch);</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;      } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;        device.parallelFor(tiling.block_mapper.blockCount(), tiling.cost,</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;                           eval_block);</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;      }</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    }</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    evaluator.cleanup();</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;  }</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;};</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160; </div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keyword">typename</span> DoneCallback, <span class="keywordtype">bool</span> Vectorizable,</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;          TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;<span class="keyword">class </span>TensorAsyncExecutor&lt;Expression, ThreadPoolDevice, DoneCallback,</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;                          Vectorizable, Tiling&gt; {</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Expression::Index StorageIndex;</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;Expression, ThreadPoolDevice&gt; Evaluator;</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> runAsync(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;                                           <span class="keyword">const</span> ThreadPoolDevice&amp; device,</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;                                           DoneCallback done) {</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;    TensorAsyncExecutorContext* <span class="keyword">const</span> ctx =</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        <span class="keyword">new</span> TensorAsyncExecutorContext(expr, device, std::move(done));</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160; </div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> on_eval_subexprs = [ctx, &amp;device](<span class="keywordtype">bool</span> need_assign) -&gt; <span class="keywordtype">void</span> {</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;      <span class="keywordflow">if</span> (!need_assign) {</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;        <span class="keyword">delete</span> ctx;</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;      }</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160; </div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;      <span class="keyword">typedef</span> EvalRange&lt;Evaluator, StorageIndex, Vectorizable&gt; EvalRange;</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;      <span class="keyword">const</span> StorageIndex size = array_prod(ctx-&gt;evaluator.dimensions());</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;      device.parallelForAsync(</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;          size, ctx-&gt;evaluator.costPerCoeff(Vectorizable),</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;          EvalRange::alignBlockSize,</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;          [ctx](StorageIndex firstIdx, StorageIndex lastIdx) {</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;            EvalRange::run(&amp;ctx-&gt;evaluator, firstIdx, lastIdx);</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;          },</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;          [ctx]() { delete ctx; });</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    };</div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160; </div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    ctx-&gt;evaluator.evalSubExprsIfNeededAsync(<span class="keyword">nullptr</span>, on_eval_subexprs);</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;  }</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160; </div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;  <span class="keyword">struct </span>TensorAsyncExecutorContext {</div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    TensorAsyncExecutorContext(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;                               <span class="keyword">const</span> ThreadPoolDevice&amp; thread_pool,</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;                               DoneCallback done)</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;        : evaluator(expr, thread_pool), on_done(std::move(done)) {}</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160; </div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    ~TensorAsyncExecutorContext() {</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;      evaluator.cleanup();</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;      on_done();</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;    }</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160; </div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    Evaluator evaluator;</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160; </div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;   <span class="keyword">private</span>:</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    DoneCallback on_done;</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;  };</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;};</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160; </div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keyword">typename</span> DoneCallback, <span class="keywordtype">bool</span> Vectorizable&gt;</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;<span class="keyword">class </span>TensorAsyncExecutor&lt;Expression, ThreadPoolDevice, DoneCallback,</div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;                          Vectorizable, <span class="comment">/*Tileable*/</span> TiledEvaluation::On&gt; {</div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits&lt;Expression&gt;::Index IndexType;</div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> traits&lt;Expression&gt;::Scalar Scalar;</div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;  <span class="keyword">typedef</span> std::remove_const_t&lt;Scalar&gt; ScalarNoConst;</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160; </div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDims = traits&lt;Expression&gt;::NumDimensions;</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160; </div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;Expression, ThreadPoolDevice&gt; Evaluator;</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;  <span class="keyword">typedef</span> TensorBlockMapper&lt;NumDims, Evaluator::Layout, IndexType&gt; BlockMapper;</div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;  <span class="keyword">typedef</span> TensorExecutorTilingContext&lt;BlockMapper&gt; TilingContext;</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160; </div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockDescriptor&lt;NumDims, IndexType&gt; TensorBlockDesc;</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockScratchAllocator&lt;ThreadPoolDevice&gt;</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;      TensorBlockScratch;</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160; </div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> runAsync(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;                                           <span class="keyword">const</span> ThreadPoolDevice&amp; device,</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;                                           DoneCallback done) {</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160; </div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    TensorAsyncExecutorContext* <span class="keyword">const</span> ctx =</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        <span class="keyword">new</span> TensorAsyncExecutorContext(expr, device, std::move(done));</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160; </div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> on_eval_subexprs = [ctx](<span class="keywordtype">bool</span> need_assign) -&gt; <span class="keywordtype">void</span> {</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;      <span class="keywordflow">if</span> (!need_assign) {</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;        <span class="keyword">delete</span> ctx;</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;        <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;      }</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160; </div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;      ctx-&gt;tiling = internal::GetTensorExecutorTilingContext&lt;</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;          Evaluator, BlockMapper, Vectorizable&gt;(ctx-&gt;evaluator);</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160; </div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;      <span class="keyword">auto</span> eval_block = [ctx](IndexType firstBlockIdx, IndexType lastBlockIdx) {</div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;        TensorBlockScratch scratch(ctx-&gt;device);</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;        <span class="keywordflow">for</span> (IndexType block_idx = firstBlockIdx; block_idx &lt; lastBlockIdx;</div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;             ++block_idx) {</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;          TensorBlockDesc desc =</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;              ctx-&gt;tiling.block_mapper.blockDescriptor(block_idx);</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;          ctx-&gt;evaluator.evalBlock(desc, scratch);</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;          scratch.reset();</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;        }</div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;      };</div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160; </div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;      <span class="comment">// Evaluate small expressions directly as a single block.</span></div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;      <span class="keywordflow">if</span> (ctx-&gt;tiling.block_mapper.blockCount() == 1) {</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;        TensorBlockScratch scratch(ctx-&gt;device);</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;        TensorBlockDesc desc(0, ctx-&gt;tiling.block_mapper.blockDimensions());</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;        ctx-&gt;evaluator.evalBlock(desc, scratch);</div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;        <span class="keyword">delete</span> ctx;</div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;      } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        ctx-&gt;device.parallelForAsync(ctx-&gt;tiling.block_mapper.blockCount(),</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;                                     ctx-&gt;tiling.cost, eval_block,</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;                                     [ctx]() { delete ctx; });</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;      }</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    };</div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160; </div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    ctx-&gt;evaluator.evalSubExprsIfNeededAsync(<span class="keyword">nullptr</span>, on_eval_subexprs);</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;  }</div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160; </div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;  <span class="keyword">struct </span>TensorAsyncExecutorContext {</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    TensorAsyncExecutorContext(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;                               <span class="keyword">const</span> ThreadPoolDevice&amp; thread_pool,</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;                               DoneCallback done)</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;        : device(thread_pool),</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;          evaluator(expr, thread_pool),</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;          on_done(std::move(done)) {}</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160; </div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    ~TensorAsyncExecutorContext() {</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;      evaluator.cleanup();</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;      on_done();</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    }</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160; </div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;    <span class="keyword">const</span> ThreadPoolDevice&amp; device;</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    Evaluator evaluator;</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    TilingContext tiling;</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160; </div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;   <span class="keyword">private</span>:</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;    DoneCallback on_done;</div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;  };</div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;};</div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160; </div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_USE_THREADS</span></div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160; </div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;<span class="comment">// GPU: the evaluation of the expression is offloaded to a GPU.</span></div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;<span class="preprocessor">#if defined(EIGEN_USE_GPU)</span></div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160; </div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keywordtype">bool</span> Vectorizable, TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a>&lt;Expression, GpuDevice, Vectorizable, Tiling&gt; {</div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Expression::Index StorageIndex;</div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;  <span class="keyword">static</span> <span class="keywordtype">void</span> run(<span class="keyword">const</span> Expression&amp; expr, <span class="keyword">const</span> GpuDevice&amp; device);</div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;};</div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160; </div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;<span class="preprocessor">#if defined(EIGEN_GPUCC)</span></div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;<span class="comment">// Returns 1 if lhs + rhs would overflow, -1 if it would underflow, otherwise 0.</span></div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Index&gt;</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE <span class="keywordtype">int</span> sum_will_overflow(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lhs,</div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;                                                            <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> rhs) {</div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;  <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> highest = NumTraits&lt;Index&gt;::highest();</div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;  <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lowest = NumTraits&lt;Index&gt;::lowest();</div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;  <span class="keywordflow">if</span> (lhs &gt; 0 &amp;&amp; rhs &gt; 0) {</div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;    <span class="keywordflow">return</span> lhs &gt; highest - rhs ? 1 : 0;</div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;  } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (lhs &lt; 0 &amp;&amp; rhs &lt; 0) {</div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    <span class="keywordflow">return</span> lhs &lt; lowest - rhs ? -1 : 0;</div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    <span class="keywordflow">return</span> 0;</div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;  }</div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;}</div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160; </div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;<span class="comment">// Returns lhs + rhs, saturating to the highest/lowest representable value on</span></div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;<span class="comment">// overflow/underflow respectively.</span></div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Index&gt;</div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> saturate_add(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lhs, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> rhs) {</div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;  <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> highest = NumTraits&lt;Index&gt;::highest();</div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;  <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lowest = NumTraits&lt;Index&gt;::lowest();</div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;  <span class="keywordtype">int</span> overflow = sum_will_overflow(lhs, rhs);</div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;  <span class="keywordflow">return</span> overflow == 1 ? highest : overflow == -1 ? lowest : lhs + rhs;</div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;}</div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160; </div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;<span class="comment">// A functor that adds step_size to a given index, saturating to avoid</span></div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;<span class="comment">// overflow/underflow. If overflow/underflow is not possible, regular addition</span></div>
<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;<span class="comment">// is used (for efficiency).</span></div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Index&gt;</div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;<span class="keyword">struct </span>SafeStep {</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;  <span class="comment">// lastIdx is one past the end of the possible indexes.</span></div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;  <span class="comment">// step_size is the value that will be added to the given index when the</span></div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;  <span class="comment">// functor is called.</span></div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SafeStep(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lastIdx, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> step_size)</div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;      : can_overflow_(sum_will_overflow(lastIdx, step_size)),</div>
<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;        step_size_(step_size) {}</div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160; </div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;  <span class="comment">// Adds step_size to index, saturating on overflow (if overflow is possible).</span></div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> operator()(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> index)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    <span class="keywordflow">return</span> can_overflow_ ? saturate_add(index, step_size_) : index + step_size_;</div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;  }</div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160; </div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">bool</span> can_overflow_;</div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;  <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> step_size_;</div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;};</div>
<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160; </div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> StorageIndex, <span class="keywordtype">bool</span> Vectorizable&gt;</div>
<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;<span class="keyword">struct </span>EigenMetaKernelEval {</div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE</div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;  <span class="keywordtype">void</span> run(Evaluator&amp; eval, StorageIndex firstIdx, StorageIndex lastIdx, StorageIndex step_size) {</div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    SafeStep&lt;StorageIndex&gt; safe_step(lastIdx, step_size);</div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;    <span class="keywordflow">for</span> (StorageIndex i = firstIdx; i &lt; lastIdx; i = safe_step(i)) {</div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;      eval.evalScalar(i);</div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;    }</div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;  }</div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;};</div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160; </div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> StorageIndex&gt;</div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;<span class="keyword">struct </span>EigenMetaKernelEval&lt;Evaluator, StorageIndex, true&gt; {</div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;  <span class="keyword">static</span> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE</div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;  <span class="keywordtype">void</span> run(Evaluator&amp; eval, StorageIndex firstIdx, StorageIndex lastIdx, StorageIndex step_size) {</div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    <span class="keyword">const</span> StorageIndex PacketSize = unpacket_traits&lt;typename Evaluator::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;    <span class="keyword">const</span> StorageIndex vectorized_size = (lastIdx / PacketSize) * PacketSize;</div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    <span class="keyword">const</span> StorageIndex vectorized_step_size = step_size * PacketSize;</div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160; </div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;    SafeStep&lt;StorageIndex&gt; safe_vectorized_step(vectorized_size,</div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;                                                vectorized_step_size);</div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;    <span class="comment">// Use the vector path</span></div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;    <span class="keywordflow">for</span> (StorageIndex i = firstIdx * PacketSize; i &lt; vectorized_size;</div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;         i = safe_vectorized_step(i)) {</div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;      eval.evalPacket(i);</div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    }</div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    SafeStep&lt;StorageIndex&gt; safe_step(lastIdx, step_size);</div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    <span class="keywordflow">for</span> (StorageIndex i = saturate_add(vectorized_size, firstIdx); i &lt; lastIdx;</div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;         i = safe_step(i)) {</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;      eval.evalScalar(i);</div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;    }</div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;  }</div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;};</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160; </div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator, <span class="keyword">typename</span> StorageIndex&gt;</div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;__global__ <span class="keywordtype">void</span></div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;__launch_bounds__(1024)</div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;EigenMetaKernel(Evaluator eval, StorageIndex size) {</div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160; </div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;  <span class="keyword">const</span> StorageIndex first_index = blockIdx.x * blockDim.x + threadIdx.x;</div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;  <span class="keyword">const</span> StorageIndex step_size = blockDim.x * gridDim.x;</div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160; </div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">bool</span> vectorizable = Evaluator::PacketAccess &amp; Evaluator::IsAligned;</div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;  EigenMetaKernelEval&lt;Evaluator, StorageIndex, vectorizable&gt;::run(eval, first_index, size, step_size);</div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;}</div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160; </div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;<span class="comment">/*static*/</span></div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keywordtype">bool</span> Vectorizable, TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;EIGEN_STRONG_INLINE <span class="keywordtype">void</span> <a class="code" href="classTensorExecutor.html">TensorExecutor&lt;Expression, GpuDevice, Vectorizable, Tiling&gt;::run</a>(</div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;    <span class="keyword">const</span> Expression&amp; expr, <span class="keyword">const</span> GpuDevice&amp; device) {</div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;  TensorEvaluator&lt;Expression, GpuDevice&gt; evaluator(expr, device);</div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(<span class="keyword">nullptr</span>);</div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;  <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160; </div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> block_size = device.maxGpuThreadsPerBlock();</div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> max_blocks =</div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;        numext::mini&lt;int64_t&gt;(device.getNumGpuMultiProcessors() *</div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;                              device.maxGpuThreadsPerMultiProcessor(),</div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;                          NumTraits&lt;StorageIndex&gt;::highest()) /</div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;        block_size;</div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;    <span class="keyword">const</span> StorageIndex size = array_prod(evaluator.dimensions());</div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;    <span class="comment">// Create a least one block to ensure we won&#39;t crash when tensorflow calls with tensors of size 0.</span></div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> num_blocks = numext::maxi&lt;int&gt;(numext::mini&lt;int&gt;(max_blocks, divup&lt;int&gt;(size, block_size)), 1);</div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160; </div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;    LAUNCH_GPU_KERNEL(</div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;        (EigenMetaKernel&lt;TensorEvaluator&lt;Expression, GpuDevice&gt;, StorageIndex&gt;),</div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;        num_blocks, block_size, 0, device, evaluator, size);</div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;  }</div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;  evaluator.cleanup();</div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;}</div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160; </div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_GPUCC</span></div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_USE_GPU</span></div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160; </div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;<span class="comment">// SYCL Executor policy</span></div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;<span class="preprocessor">#ifdef EIGEN_USE_SYCL</span></div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160; </div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Evaluator&gt;</div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;<span class="keyword">struct </span>ExecExprFunctorKernel {</div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Evaluator::Index <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;  Evaluator evaluator;</div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;  <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> range;</div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Scratch&gt;</div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ExecExprFunctorKernel(</div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;      <span class="keyword">const</span> Scratch, Evaluator evaluator_, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> range_)</div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;      : evaluator(evaluator_), range(range_) {}</div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160; </div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE <span class="keywordtype">void</span> operator()(</div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;      cl::sycl::nd_item&lt;1&gt; itemID) {</div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;    compute(itemID);</div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;  }</div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> is_vec = Evaluator::PacketAccess&gt;</div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t&lt;!is_vec&gt;</div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;  compute(<span class="keyword">const</span> cl::sycl::nd_item&lt;1&gt;&amp; itemID) {</div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> gId = <span class="keyword">static_cast&lt;</span><a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a><span class="keyword">&gt;</span>(itemID.get_global_linear_id());</div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> total_threads = itemID.get_global_range(0);</div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160; </div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;    <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i = gId; i &lt; range; i += total_threads) {</div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;      evaluator.evalScalar(i);</div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;    }</div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;  }</div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> is_vec = Evaluator::PacketAccess&gt;</div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t&lt;is_vec&gt;</div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;  compute(<span class="keyword">const</span> cl::sycl::nd_item&lt;1&gt;&amp; itemID) {</div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> vectorizedRange =</div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;        (range / Evaluator::PacketSize) * Evaluator::PacketSize;</div>
<div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> gId = <span class="keyword">static_cast&lt;</span><a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a><span class="keyword">&gt;</span>(itemID.get_global_linear_id());</div>
<div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> step = Evaluator::PacketSize * itemID.get_global_range(0);</div>
<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> start = Evaluator::PacketSize * gId;</div>
<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;    <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i = start; i &lt; vectorizedRange; i += step) {</div>
<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;      evaluator.evalPacket(i);</div>
<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;    }</div>
<div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;    gId += vectorizedRange;</div>
<div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i = gId; i &lt; range; i += itemID.get_global_range(0)) {</div>
<div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;      evaluator.evalScalar(i);</div>
<div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;    }</div>
<div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;  }</div>
<div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;};</div>
<div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160; </div>
<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Expression, <span class="keywordtype">bool</span> Vectorizable, TiledEvaluation Tiling&gt;</div>
<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;<span class="keyword">class </span><a class="code" href="classTensorExecutor.html">TensorExecutor</a>&lt;Expression, <a class="code" href="namespaceEigen.html">Eigen</a>::SyclDevice, Vectorizable, Tiling&gt; {</div>
<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160; <span class="keyword">public</span>:</div>
<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Expression::Index <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;  <span class="keyword">static</span> EIGEN_STRONG_INLINE <span class="keywordtype">void</span> run(<span class="keyword">const</span> Expression&amp; expr,</div>
<div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;                                      <span class="keyword">const</span> Eigen::SyclDevice&amp; dev) {</div>
<div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;    <span class="keyword">typedef</span> <a class="code" href="structEigen_1_1TensorEvaluator.html">Eigen::TensorEvaluator&lt;Expression, Eigen::SyclDevice&gt;</a> Evaluator;</div>
<div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;    Evaluator evaluator(expr, dev);</div>
<div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_assign = evaluator.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;    <span class="keywordflow">if</span> (needs_assign) {</div>
<div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> range, GRange, tileSize;</div>
<div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> total_size = ::Eigen::internal::array_prod(evaluator.dimensions());</div>
<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;      total_size = (total_size == 0) ? 1 : total_size;</div>
<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> PacketSize =</div>
<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;          Eigen::PacketType&lt;<span class="keyword">typename</span> Evaluator::CoeffReturnType,</div>
<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;                            Eigen::SyclDevice&gt;::size;</div>
<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> vectorizable_threads = <span class="keyword">static_cast&lt;</span><a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a><span class="keyword">&gt;</span>(total_size / PacketSize);</div>
<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;      dev.parallel_for_setup(vectorizable_threads, tileSize, range, GRange);</div>
<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;      range = total_size;</div>
<div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160; </div>
<div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;      dev.template nullary_kernel_launcher&lt;</div>
<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;          <span class="keyword">typename</span> Evaluator::CoeffReturnType,</div>
<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;          ExecExprFunctorKernel&lt;Evaluator&gt; &gt;(</div>
<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;          evaluator,</div>
<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;          cl::sycl::nd_range&lt;1&gt;(cl::sycl::range&lt;1&gt;(GRange),</div>
<div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;                                cl::sycl::range&lt;1&gt;(tileSize)),</div>
<div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;          <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>(1), range);</div>
<div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;    }</div>
<div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;    evaluator.cleanup();</div>
<div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;  }</div>
<div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;};</div>
<div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160; </div>
<div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160; </div>
<div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;} <span class="comment">// end namespace internal</span></div>
<div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160; </div>
<div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;} <span class="comment">// end namespace Eigen</span></div>
<div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160; </div>
<div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H</span></div>
<div class="ttc" id="aclassTensorExecutor_html"><div class="ttname"><a href="classTensorExecutor.html">TensorExecutor</a></div><div class="ttdoc">The tensor executor class.</div></div>
<div class="ttc" id="anamespaceEigen_html"><div class="ttname"><a href="namespaceEigen.html">Eigen</a></div><div class="ttdoc">Namespace containing all symbols from the Eigen library.</div></div>
<div class="ttc" id="anamespaceEigen_html_a62e77e0933482dafde8fe197d9a2cfde"><div class="ttname"><a href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Eigen::Index</a></div><div class="ttdeci">EIGEN_DEFAULT_DENSE_INDEX_TYPE Index</div></div>
<div class="ttc" id="astructEigen_1_1TensorEvaluator_html"><div class="ttname"><a href="structEigen_1_1TensorEvaluator.html">Eigen::TensorEvaluator</a></div><div class="ttdoc">A cost model used to limit the number of threads used for evaluating tensor expression.</div><div class="ttdef"><b>Definition:</b> TensorEvaluator.h:31</div></div>
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